Recognition of dependences on the basis of inverse mapping

The article describes the method of recognition of dependences based on the use of inverse mapping. From a given finite set of models, one chooses the model that best fits the sample data. For each model, the selective dependence corresponding to it is determined by the sample. For the one-dimensional case, each selective dependence is mapped to the same reference model in the form of the straight line equation by means of inverse mapping. For each model, sample data are mapped to the same equation of the straight line with some mistakes. It is suggested to use the minimum of variance of mistakes as the criterion of adequacy of the constructed model of sample of data. In the case of multidimensional dependences, a heuristic method is suggested according to which a set of inverse functions for each of explanatory variables is considered for each model. Approbation of the method by means of statistical modeling by the Monte-Carlo method is carried out.